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import requests
import json
import pandas as pd
import sys
sys.path.insert(0,"/Users/he/work/gwaslab/src")
import gwaslab as gl
import requests
import json
import pandas as pd
import sys
sys.path.insert(0,"/Users/he/work/gwaslab/src")
import gwaslab as gl
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mysumstats = gl.Sumstats("t2d_bbj.txt.gz",
snpid="SNP",
chrom="CHR",
pos="POS",
ea="ALT",
nea="REF",
neaf="Frq",
beta="BETA",
se="SE",
p="P",
direction="Dir",
n="N",nrows=1000000)
mysumstats = gl.Sumstats("t2d_bbj.txt.gz",
snpid="SNP",
chrom="CHR",
pos="POS",
ea="ALT",
nea="REF",
neaf="Frq",
beta="BETA",
se="SE",
p="P",
direction="Dir",
n="N",nrows=1000000)
Tue Dec 13 14:19:23 2022 Start to initiate from file :t2d_bbj.txt.gz Tue Dec 13 14:19:26 2022 -Reading columns : CHR,SE,N,Dir,POS,SNP,ALT,REF,Frq,BETA,P Tue Dec 13 14:19:26 2022 -Renaming columns to : CHR,SE,N,DIRECTION,POS,SNPID,EA,NEA,EAF,BETA,P Tue Dec 13 14:19:26 2022 -Current Dataframe shape : 1000000 x 11 Tue Dec 13 14:19:26 2022 -Initiating a status column: STATUS ... Tue Dec 13 14:19:28 2022 -NEAF is specified... Tue Dec 13 14:19:28 2022 -Checking if 0<= NEAF <=1 ... Tue Dec 13 14:19:28 2022 -Converted NEAF to EAF. Tue Dec 13 14:19:28 2022 -Removed 0 variants with bad NEAF. Tue Dec 13 14:19:28 2022 Start to reorder the columns... Tue Dec 13 14:19:28 2022 -Current Dataframe shape : 1000000 x 12 Tue Dec 13 14:19:28 2022 -Reordering columns to : SNPID,CHR,POS,EA,NEA,EAF,BETA,SE,P,N,DIRECTION,STATUS Tue Dec 13 14:19:28 2022 Finished sorting columns successfully! Tue Dec 13 14:19:28 2022 Finished loading data successfully!
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mysumstats.filter_value('CHR =="1"',inplace=True)
mysumstats.filter_value('CHR =="1"',inplace=True)
Tue Dec 13 14:19:29 2022 Start filtering values by condition: CHR =="1" Tue Dec 13 14:19:29 2022 -Removing 32832 variants not meeting the conditions: CHR =="1" Tue Dec 13 14:19:29 2022 Finished filtering values.
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mysumstats.data.loc[mysumstats.data["SNPID"]=="1:751343_T_A","POS"]=int(43430)
mysumstats.data.loc[mysumstats.data["SNPID"]=="1:751343_T_A","P"]=1e-8
mysumstats.data.loc[mysumstats.data["SNPID"]=="1:751343_T_A","POS"]=int(43430)
mysumstats.data.loc[mysumstats.data["SNPID"]=="1:751343_T_A","P"]=1e-8
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mysumstats.basic_check()
mysumstats.basic_check()
Tue Dec 13 14:19:29 2022 Start to check IDs... Tue Dec 13 14:19:29 2022 -Current Dataframe shape : 967168 x 12 Tue Dec 13 14:19:29 2022 -Checking if SNPID is chr:pos:ref:alt...(separator: - ,: , _) Tue Dec 13 14:19:32 2022 Finished checking IDs successfully! Tue Dec 13 14:19:32 2022 Start to fix chromosome notation... Tue Dec 13 14:19:32 2022 -Current Dataframe shape : 967168 x 12 Tue Dec 13 14:19:43 2022 -Vairants with standardized chromosome notation: 967168 Tue Dec 13 14:19:49 2022 -All CHR are already fixed... Tue Dec 13 14:19:52 2022 Finished fixing chromosome notation successfully! Tue Dec 13 14:19:52 2022 Start to fix basepair positions... Tue Dec 13 14:19:52 2022 -Current Dataframe shape : 967168 x 12 Tue Dec 13 14:19:52 2022 -Converting to Int64 data type ... Tue Dec 13 14:19:54 2022 -Position upper_bound is: 250,000,000 Tue Dec 13 14:19:59 2022 -Remove outliers: 0 Tue Dec 13 14:20:00 2022 -Converted all position to datatype Int64. Tue Dec 13 14:20:00 2022 Finished fixing basepair position successfully! Tue Dec 13 14:20:00 2022 Start to fix alleles... Tue Dec 13 14:20:00 2022 -Current Dataframe shape : 967168 x 12 Tue Dec 13 14:20:01 2022 -Detected 0 variants with alleles that contain bases other than A/C/T/G . Tue Dec 13 14:20:02 2022 -Converted all bases to string datatype and UPPERCASE. Tue Dec 13 14:20:05 2022 Finished fixing allele successfully! Tue Dec 13 14:20:05 2022 Start sanity check for statistics ... Tue Dec 13 14:20:05 2022 -Current Dataframe shape : 967168 x 12 Tue Dec 13 14:20:05 2022 -Checking if 0 <=N<= inf ... Tue Dec 13 14:20:05 2022 -Removed 0 variants with bad N. Tue Dec 13 14:20:05 2022 -Checking if 0 <=EAF<= 1 ... Tue Dec 13 14:20:06 2022 -Removed 0 variants with bad EAF. Tue Dec 13 14:20:06 2022 -Checking if 5 <=MAC<= inf ... Tue Dec 13 14:20:06 2022 -Removed 0 variants with bad MAC. Tue Dec 13 14:20:06 2022 -Checking if 5e-300 <= P <= 1 ... Tue Dec 13 14:20:06 2022 -Removed 0 variants with bad P. Tue Dec 13 14:20:06 2022 -Checking if -10 <BETA)< 10 ... Tue Dec 13 14:20:06 2022 -Removed 0 variants with bad BETA. Tue Dec 13 14:20:06 2022 -Checking if 0 <SE< inf ... Tue Dec 13 14:20:06 2022 -Removed 0 variants with bad SE. Tue Dec 13 14:20:06 2022 -Checking STATUS... Tue Dec 13 14:20:07 2022 -Coverting STAUTUS to interger. Tue Dec 13 14:20:07 2022 -Removed 0 variants with bad statistics in total. Tue Dec 13 14:20:07 2022 Finished sanity check successfully! Tue Dec 13 14:20:07 2022 Start to normalize variants... Tue Dec 13 14:20:07 2022 -Current Dataframe shape : 967168 x 12 Tue Dec 13 14:20:09 2022 -No available variants to normalize.. Tue Dec 13 14:20:09 2022 Finished normalizing variants successfully!
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mysumstats.data
mysumstats.data
Out[6]:
| SNPID | CHR | POS | EA | NEA | EAF | BETA | SE | P | N | DIRECTION | STATUS | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1:725932_G_A | 1 | 725932 | G | A | 0.9960 | -0.0737 | 0.1394 | 5.970000e-01 | 166718 | -?+- | 9960099 |
| 1 | 1:725933_A_G | 1 | 725933 | G | A | 0.0040 | 0.0737 | 0.1394 | 5.973000e-01 | 166718 | +?-+ | 9960099 |
| 2 | 1:737801_T_C | 1 | 737801 | C | T | 0.0051 | 0.0490 | 0.1231 | 6.908000e-01 | 166718 | +?-+ | 9960099 |
| 3 | 1:749963_T_TAA | 1 | 749963 | TAA | T | 0.8374 | 0.0213 | 0.0199 | 2.846000e-01 | 166718 | -?++ | 9960399 |
| 4 | 1:751343_T_A | 1 | 43430 | T | A | 0.8593 | 0.0172 | 0.0156 | 1.000000e-08 | 166718 | -?++ | 9960099 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 967163 | 1:249229516_G_C | 1 | 249229516 | G | C | 0.9919 | -0.0047 | 0.0822 | 9.544000e-01 | 191764 | +--+ | 9960099 |
| 967164 | 1:249229680_G_C | 1 | 249229680 | G | C | 0.9933 | -0.0744 | 0.0883 | 3.992000e-01 | 191764 | --++ | 9960099 |
| 967165 | 1:249229734_T_C | 1 | 249229734 | C | T | 0.0556 | 0.0356 | 0.0286 | 2.135000e-01 | 191764 | -+++ | 9960099 |
| 967166 | 1:249230910_C_T | 1 | 249230910 | C | T | 0.9502 | -0.0507 | 0.0345 | 1.413000e-01 | 191764 | +--- | 9960099 |
| 967167 | 1:249232965_C_T | 1 | 249232965 | C | T | 0.9560 | -0.0308 | 0.0317 | 3.311000e-01 | 191764 | +--- | 9960099 |
967168 rows × 12 columns
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mysumstats.data.loc[mysumstats.data["SNPID"]=="1:751343_T_A","POS"]=int(7026152)
mysumstats.data.loc[mysumstats.data["SNPID"]=="1:751343_T_A","P"]=1e-8
mysumstats.data.loc[mysumstats.data["SNPID"]=="1:751343_T_A","POS"]=int(7026152)
mysumstats.data.loc[mysumstats.data["SNPID"]=="1:751343_T_A","P"]=1e-8
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mysumstats.plot_mqq(mode="r",
region=(1,6500000,7500000),
taf=[4,0,0.5,1,1],
gtf_path="~/mydata/dog/Canis_lupus_familiaris.CanFam3.1.104.chr.gtf.gz",
region_protein_coding=False,
vcf_path="~/mydata/dog/dogs.590publicSamples.vcf.gz",
rr_path="~/mydata/dog/chr1_average_canFam3.1.txt",
rr_header_dict={"pos":"Position(bp)","rate":"Rate(cM/Mb)"})
mysumstats.plot_mqq(mode="r",
region=(1,6500000,7500000),
taf=[4,0,0.5,1,1],
gtf_path="~/mydata/dog/Canis_lupus_familiaris.CanFam3.1.104.chr.gtf.gz",
region_protein_coding=False,
vcf_path="~/mydata/dog/dogs.590publicSamples.vcf.gz",
rr_path="~/mydata/dog/chr1_average_canFam3.1.txt",
rr_header_dict={"pos":"Position(bp)","rate":"Rate(cM/Mb)"})
Tue Dec 13 14:28:42 2022 Start to plot manhattan/qq plot with the following basic settings: Tue Dec 13 14:28:42 2022 -Genome-wide significance level is set to 5e-08 ... Tue Dec 13 14:28:42 2022 -Raw input contains 967168 variants... Tue Dec 13 14:28:42 2022 -Plot layout mode is : r Tue Dec 13 14:28:42 2022 -Region to plot : chr1:6500000-7500000. Tue Dec 13 14:28:42 2022 -Extract SNPs in region : chr1:6500000-7500000... Tue Dec 13 14:28:44 2022 -Extract SNPs in specified regions: 4622 Tue Dec 13 14:28:44 2022 Finished loading specified columns from the sumstats. Tue Dec 13 14:28:44 2022 Start conversion and sanity check: Tue Dec 13 14:28:44 2022 -Removed 0 variants with nan in CHR or POS column ... Tue Dec 13 14:28:44 2022 -Removed 0 variants with nan in P column ... Tue Dec 13 14:28:44 2022 -P values are being converted to -log10(P)... Tue Dec 13 14:28:44 2022 -Sanity check after conversion: 0 variants with P value outside of (0,1] will be removed... Tue Dec 13 14:28:44 2022 -Sanity check: 0 na/inf/-inf variants will be removed... Tue Dec 13 14:28:44 2022 -Maximum -log10(P) values is 8.0 . Tue Dec 13 14:28:44 2022 Finished data conversion and sanity check. Tue Dec 13 14:28:44 2022 Start to load reference genotype... Tue Dec 13 14:28:44 2022 -reference vcf path : /Users/he/mydata/dog/dogs.590publicSamples.vcf.gz Tue Dec 13 14:28:47 2022 -Retrieving index... Tue Dec 13 14:28:47 2022 -Ref variants in the region: 18139 Tue Dec 13 14:28:47 2022 -Calculating Rsq... Tue Dec 13 14:28:47 2022 Finished loading reference genotype successfully! Tue Dec 13 14:28:47 2022 Start to create manhattan plot with 4622 variants: Tue Dec 13 14:28:47 2022 -Loading gtf files from:/Users/he/mydata/dog/Canis_lupus_familiaris.CanFam3.1.104.chr.gtf.gz Tue Dec 13 14:28:55 2022 -plotting gene track.. Tue Dec 13 14:28:55 2022 -Finished plotting gene track.. Tue Dec 13 14:28:55 2022 -Found 1 significant variants with a sliding window size of 500 kb... Tue Dec 13 14:28:55 2022 Finished creating Manhattan plot successfully! Tue Dec 13 14:28:55 2022 -Skip annotating
Out[10]:
(<Figure size 3000x2000 with 4 Axes>, <gwaslab.Log.Log at 0x7fe4bb70a7f0>)
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import scipy as sp
import scipy as sp
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df = 1
sp.stats.norm.rvs(df, scale=1, size=1, random_state=None)
df = 1
sp.stats.norm.rvs(df, scale=1, size=1, random_state=None)
Out[13]:
array([1.15757894])
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sp.stats.norm.rvs(df, scale=1, size=1, random_state=None)
sp.stats.norm.rvs(df, scale=1, size=1, random_state=None)
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sp.stats.norm.pdf(0, loc=0, scale=1)
sp.stats.norm.pdf(0, loc=0, scale=1)
Out[22]:
0.3989422804014327
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sp.stats.norm.cdf(0, loc=0, scale=1)
sp.stats.norm.cdf(0, loc=0, scale=1)
Out[23]:
0.5
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sp.stats.norm.ppf(0.975)
sp.stats.norm.ppf(0.975)
Out[29]:
1.959963984540054
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sp.stats.norm.sf(1.96, loc=0, scale=1)
sp.stats.norm.sf(1.96, loc=0, scale=1)
Out[26]:
0.024997895148220435
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sp.stats.norm.isf(0.025, loc=0, scale=1)
sp.stats.norm.isf(0.025, loc=0, scale=1)
Out[27]:
1.9599639845400545
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sp.stats.norm.interval(0.95, loc=0, scale=1)
sp.stats.norm.interval(0.95, loc=0, scale=1)
Out[30]:
(-1.959963984540054, 1.959963984540054)
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print(gl.__file__)
print(gl.__file__)
/Users/he/opt/anaconda3/lib/python3.8/site-packages/gwaslab/__init__.py
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#import sys
#sys.path.insert(0,"/home/yunye/gwaslab/gwaslab/src")
import gwaslab as gl
#import sys
#sys.path.insert(0,"/home/yunye/gwaslab/gwaslab/src")
import gwaslab as gl
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mysumstats = gl.Sumstats("t2d_bbj.txt.gz",
snpid="SNP",
chrom="CHR",
pos="POS",
ea="ALT",
nea="REF",
neaf="Frq",
beta="BETA",
se="SE",
p="P",
direction="Dir",
n="N")
mysumstats = gl.Sumstats("t2d_bbj.txt.gz",
snpid="SNP",
chrom="CHR",
pos="POS",
ea="ALT",
nea="REF",
neaf="Frq",
beta="BETA",
se="SE",
p="P",
direction="Dir",
n="N")
Mon Dec 12 23:51:59 2022 Start to initiate from file :t2d_bbj.txt.gz Mon Dec 12 23:52:26 2022 -Reading columns : SE,Dir,REF,BETA,SNP,CHR,ALT,Frq,P,POS,N Mon Dec 12 23:52:26 2022 -Renaming columns to : SE,DIRECTION,NEA,BETA,SNPID,CHR,EA,EAF,P,POS,N Mon Dec 12 23:52:26 2022 -Current Dataframe shape : 12557761 x 11 Mon Dec 12 23:52:33 2022 -Initiating a status column: STATUS ... Mon Dec 12 23:52:37 2022 -NEAF is specified... Mon Dec 12 23:52:37 2022 -Checking if 0<= NEAF <=1 ... Mon Dec 12 23:52:41 2022 -Converted NEAF to EAF. Mon Dec 12 23:52:41 2022 -Removed 0 variants with bad NEAF. Mon Dec 12 23:52:41 2022 Start to reorder the columns... Mon Dec 12 23:52:41 2022 -Current Dataframe shape : 12557761 x 12 Mon Dec 12 23:52:41 2022 -Reordering columns to : SNPID,CHR,POS,EA,NEA,EAF,BETA,SE,P,N,DIRECTION,STATUS Mon Dec 12 23:52:42 2022 Finished sorting columns successfully! Mon Dec 12 23:52:42 2022 Finished loading data successfully!
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pd.api.types.is_string_dtype(mysumstats.data["CHR"])
pd.api.types.is_string_dtype(mysumstats.data["CHR"])
Out[3]:
True
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mysumstats.plot_mqq(skip=2,stratified=True,include_chrXYMT=True)
mysumstats.plot_mqq(skip=2,stratified=True,include_chrXYMT=True)
Mon Dec 12 23:52:42 2022 Start to plot manhattan/qq plot with the following basic settings: Mon Dec 12 23:52:42 2022 -Genome-wide significance level is set to 5e-08 ... Mon Dec 12 23:52:42 2022 -Raw input contains 12557761 variants... Mon Dec 12 23:52:42 2022 -Plot layout mode is : mqq Mon Dec 12 23:52:46 2022 Finished loading specified columns from the sumstats. Mon Dec 12 23:52:46 2022 Start conversion and sanity check: Mon Dec 12 23:52:49 2022 -Removed 0 variants with nan in CHR or POS column ... Mon Dec 12 23:52:49 2022 -Removed 0 variants with nan in EAF column ... Mon Dec 12 23:52:50 2022 -Removed 0 variants with nan in P column ... Mon Dec 12 23:52:50 2022 -P values are being converted to -log10(P)... Mon Dec 12 23:52:50 2022 -Sanity check after conversion: 0 variants with P value outside of (0,1] will be removed... Mon Dec 12 23:52:53 2022 -Sanity check: 0 na/inf/-inf variants will be removed... Mon Dec 12 23:52:57 2022 -Maximum -log10(P) values is 167.58838029403677 . Mon Dec 12 23:52:57 2022 Finished data conversion and sanity check. Mon Dec 12 23:52:57 2022 Start to create manhattan plot with 332882 variants: Mon Dec 12 23:52:59 2022 -Found 89 significant variants with a sliding window size of 500 kb... Mon Dec 12 23:52:59 2022 Finished creating Manhattan plot successfully! Mon Dec 12 23:52:59 2022 -Skip annotating Mon Dec 12 23:52:59 2022 Start to create QQ plot with 332882 variants: Mon Dec 12 23:53:01 2022 -Calculating lambda GC: 1.2128258399293808 Mon Dec 12 23:53:01 2022 Finished creating QQ plot successfully!
Out[4]:
(<Figure size 3000x1000 with 2 Axes>, <gwaslab.Log.Log at 0x7fabaf8336d0>)
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mysumstats.plot_mqq(skip=2,stratified=True,include_chrXYMT=False)
mysumstats.plot_mqq(skip=2,stratified=True,include_chrXYMT=False)
Mon Dec 12 23:53:10 2022 Start to plot manhattan/qq plot with the following basic settings: Mon Dec 12 23:53:10 2022 -Genome-wide significance level is set to 5e-08 ... Mon Dec 12 23:53:10 2022 -Raw input contains 12557761 variants... Mon Dec 12 23:53:10 2022 -Plot layout mode is : mqq Mon Dec 12 23:53:15 2022 Finished loading specified columns from the sumstats. Mon Dec 12 23:53:15 2022 Start conversion and sanity check: Mon Dec 12 23:53:18 2022 -Removed 0 variants with nan in CHR or POS column ... Mon Dec 12 23:53:19 2022 -Removed 0 variants with nan in EAF column ... Mon Dec 12 23:53:19 2022 -Removed 0 variants with nan in P column ... Mon Dec 12 23:53:19 2022 -P values are being converted to -log10(P)... Mon Dec 12 23:53:19 2022 -Sanity check after conversion: 0 variants with P value outside of (0,1] will be removed... Mon Dec 12 23:53:22 2022 -Sanity check: 0 na/inf/-inf variants will be removed... Mon Dec 12 23:53:26 2022 -Maximum -log10(P) values is 167.58838029403677 . Mon Dec 12 23:53:26 2022 Finished data conversion and sanity check. Mon Dec 12 23:53:27 2022 Start to create manhattan plot with 332882 variants: Mon Dec 12 23:53:29 2022 -Found 89 significant variants with a sliding window size of 500 kb... Mon Dec 12 23:53:29 2022 Finished creating Manhattan plot successfully! Mon Dec 12 23:53:29 2022 -Skip annotating Mon Dec 12 23:53:29 2022 Start to create QQ plot with 332882 variants: Mon Dec 12 23:53:31 2022 -Excluding chrX,Y, MT from calculation of lambda GC. Mon Dec 12 23:53:38 2022 -Calculating lambda GC: 1.2139048028292598 Mon Dec 12 23:53:38 2022 Finished creating QQ plot successfully!
Out[5]:
(<Figure size 3000x1000 with 2 Axes>, <gwaslab.Log.Log at 0x7fabaf8336d0>)
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mysumstats.get_lead(
windowsizekb=500,
sig_level=5e-8,
anno=True,
build="19",
source="ensembl",
verbose=True)
mysumstats.get_lead(
windowsizekb=500,
sig_level=5e-8,
anno=True,
build="19",
source="ensembl",
verbose=True)
Mon Dec 12 23:53:46 2022 Start to extract lead variants... Mon Dec 12 23:53:46 2022 -Processing 12557761 variants... Mon Dec 12 23:53:46 2022 -Significance threshold : 5e-08 Mon Dec 12 23:53:46 2022 -Sliding window size: 500 kb Mon Dec 12 23:54:05 2022 -Found 9461 significant variants in total...
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-6-c955e83e26f9> in <module> ----> 1 mysumstats.get_lead( 2 windowsizekb=500, 3 sig_level=5e-8, 4 anno=True, 5 build="19", ~/work/gwaslab/src/gwaslab/Sumstats.py in get_lead(self, **args) 403 else: 404 id_to_use = "rsID" --> 405 output = getsig(self.data, 406 id=id_to_use, 407 chrom="CHR", ~/work/gwaslab/src/gwaslab/getsig.py in getsig(insumstats, id, chrom, pos, p, windowsizekb, sig_level, log, xymt, anno, build, source, verbose) 73 for line_number,(index, row) in enumerate(sumstats_sig.iterrows()): 74 #when finished one chr ---> 75 if row[chrom]!=current_sig_chr: 76 #add the current lead variants id to lead variant list 77 if current_sig_index is not False:sig_index_list.append(current_sig_index) ~/opt/anaconda3/lib/python3.8/site-packages/pandas/_libs/missing.pyx in pandas._libs.missing.NAType.__bool__() TypeError: boolean value of NA is ambiguous
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# Set variables object of arguments to be passed to endpoint
# Set base URL of GraphQL API endpoint
base_url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/MONDO_0004247/associations?projection=associationByEfoTrait"
# Perform POST request and check status code of response
r = requests.get(base_url)
print(r.status_code)
# Transform API response from JSON into Python dictionary and print in console
api_response = json.loads(r.text)
api_response
# Set variables object of arguments to be passed to endpoint
# Set base URL of GraphQL API endpoint
base_url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/MONDO_0004247/associations?projection=associationByEfoTrait"
# Perform POST request and check status code of response
r = requests.get(base_url)
print(r.status_code)
# Transform API response from JSON into Python dictionary and print in console
api_response = json.loads(r.text)
api_response
200
Out[2]:
{'_embedded': {'associations': [{'range': None,
'snps': [{'rsId': 'rs12532051',
'merged': 0,
'functionalClass': 'intron_variant',
'lastUpdateDate': '2021-06-25T04:18:22.499+0000',
'locations': [{'chromosomeName': '7',
'chromosomePosition': 148109889,
'region': {'name': '7q35'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12532051{?projection}',
'templated': True}}}],
'genomicContexts': [{'isIntergenic': False,
'isUpstream': False,
'isDownstream': False,
'distance': 0,
'gene': {'geneName': 'CNTNAP2',
'entrezGeneIds': [{'entrezGeneId': '26047'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000278728'},
{'ensemblGeneId': 'ENSG00000174469'}]},
'location': {'chromosomeName': '7',
'chromosomePosition': 148109889,
'region': {'name': '7q35'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12532051{?projection}',
'templated': True}}},
'source': 'Ensembl',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12532051{?projection}',
'templated': True}}},
{'isIntergenic': False,
'isUpstream': False,
'isDownstream': False,
'distance': 0,
'gene': {'geneName': 'CNTNAP2',
'entrezGeneIds': [{'entrezGeneId': '26047'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000278728'},
{'ensemblGeneId': 'ENSG00000174469'}]},
'location': {'chromosomeName': '7',
'chromosomePosition': 148109889,
'region': {'name': '7q35'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12532051{?projection}',
'templated': True}}},
'source': 'NCBI',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12532051{?projection}',
'templated': True}}}]}],
'loci': [{'haplotypeSnpCount': None,
'description': 'Single variant',
'strongestRiskAlleles': [{'riskAlleleName': 'rs12532051-C',
'riskFrequency': '0.96',
'genomeWide': False,
'limitedList': False,
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'_links': {'snps': [{'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12678747{?projection}',
'templated': True},
{'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12678747{?projection}',
'templated': True}]}},
'source': 'NCBI',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12678747{?projection}',
'templated': True}}},
{'isIntergenic': True,
'isUpstream': True,
'isDownstream': False,
'distance': 135906,
'gene': {'geneName': 'XKR9',
'entrezGeneIds': [{'entrezGeneId': '389668'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000221947'}]},
'location': {'chromosomeName': '8',
'chromosomePosition': 71202541,
'region': {'name': '8q13.3'},
'_links': {'snps': [{'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12678747{?projection}',
'templated': True},
{'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12678747{?projection}',
'templated': True}]}},
'source': 'NCBI',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': True,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12678747{?projection}',
'templated': True}}}]}],
'loci': [{'haplotypeSnpCount': None,
'description': 'Single variant',
'strongestRiskAlleles': [{'riskAlleleName': 'rs12678747-T',
'riskFrequency': None,
'genomeWide': False,
'limitedList': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs12678747{?projection}',
'templated': True}}}],
'authorReportedGenes': []}],
'riskFrequency': 'NR',
'study': {'initialSampleSize': '235 European ancestry cases, 441 European ancestry controls',
'gxe': False,
'gxg': False,
'snpCount': 5548084,
'qualifier': None,
'imputed': True,
'pooled': False,
'studyDesignComment': None,
'accessionId': 'GCST90096798',
'fullPvalueSet': False,
'userRequested': False,
'platforms': [{'manufacturer': 'Illumina'}],
'ancestries': [{'type': 'initial',
'numberOfIndividuals': 676,
'ancestralGroups': [{'ancestralGroup': 'European'}],
'countryOfOrigin': [],
'countryOfRecruitment': [{'majorArea': 'Europe',
'region': 'Northern Europe',
'countryName': 'U.K.'}]},
{'type': 'replication',
'numberOfIndividuals': 515,
'ancestralGroups': [{'ancestralGroup': 'European'}],
'countryOfOrigin': [],
'countryOfRecruitment': [{'majorArea': 'Europe',
'region': 'Northern Europe',
'countryName': 'U.K.'}]}],
'diseaseTrait': {'trait': 'NSAID-induced peptic ulcer disease'},
'genotypingTechnologies': [{'genotypingTechnology': 'Genome-wide genotyping array'}],
'replicationSampleSize': '206 European ancestry cases, 309 European ancestry controls',
'publicationInfo': {'pubmedId': '34864618',
'publicationDate': '2021-12-02',
'publication': 'EBioMedicine',
'title': 'Genome-Wide association between EYA1 and Aspirin-induced peptic ulceration.',
'author': {'fullname': 'Bourgeois S', 'orcid': None}}},
'pvalueDescription': None,
'orPerCopyNum': 2.03,
'snpType': 'novel',
'multiSnpHaplotype': False,
'snpInteraction': False,
'pvalueMantissa': 3,
'pvalueExponent': -11,
'standardError': None,
'pvalue': 3e-11,
'betaNum': None,
'betaUnit': None,
'betaDirection': None,
'description': None,
'_links': {'self': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/95284575'},
'association': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/95284575{?projection}',
'templated': True},
'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/95284575/snps'},
'backgroundEfoTraits': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/95284575/backgroundEfoTraits'},
'study': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/95284575/study'},
'efoTraits': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/95284575/efoTraits'}}}]},
'_links': {'self': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/efoTraits/MONDO_0004247/associations?projection=associationByEfoTrait'}}}
In [37]:
Copied!
for i in api_response["_embedded"]["associations"]:
#if i is dict:
print(i["snps"][0]["genomicContexts"][0].keys())
for i in api_response["_embedded"]["associations"]:
#if i is dict:
print(i["snps"][0]["genomicContexts"][0].keys())
dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links']) dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links']) dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links']) dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links']) dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links']) dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links']) dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links']) dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links']) dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links']) dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links']) dict_keys(['isIntergenic', 'isUpstream', 'isDownstream', 'distance', 'gene', 'location', 'source', 'mappingMethod', 'isClosestGene', '_links'])
--------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipython-input-37-b92f79f249ee> in <module> 1 for i in api_response["_embedded"]["associations"]: 2 #if i is dict: ----> 3 print(i["snps"][0]["genomicContexts"][0].keys()) 4 IndexError: list index out of range
In [115]:
Copied!
api_response["_embedded"]["associations"]
api_response["_embedded"]["associations"]
Out[115]:
[{'range': '[1.31-1.49]',
'snps': [{'rsId': 'rs75021220',
'merged': 0,
'functionalClass': 'intergenic_variant',
'lastUpdateDate': '2021-06-24T20:23:26.532+0000',
'locations': [{'chromosomeName': '4',
'chromosomePosition': 110737238,
'region': {'name': '4q25'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs75021220{?projection}',
'templated': True}}}],
'genomicContexts': [{'isIntergenic': True,
'isUpstream': True,
'isDownstream': False,
'distance': 95115,
'gene': {'geneName': 'PITX2',
'entrezGeneIds': [{'entrezGeneId': '5308'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000164093'}]},
'location': {'chromosomeName': '4',
'chromosomePosition': 110737238,
'region': {'name': '4q25'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs75021220{?projection}',
'templated': True}}},
'source': 'Ensembl',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': True,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs75021220{?projection}',
'templated': True}}},
{'isIntergenic': True,
'isUpstream': False,
'isDownstream': True,
'distance': 57165,
'gene': {'geneName': 'LINC01438',
'entrezGeneIds': [],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000249519'}]},
'location': {'chromosomeName': '4',
'chromosomePosition': 110737238,
'region': {'name': '4q25'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs75021220{?projection}',
'templated': True}}},
'source': 'Ensembl',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': True,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs75021220{?projection}',
'templated': True}}},
{'isIntergenic': True,
'isUpstream': True,
'isDownstream': False,
'distance': 95115,
'gene': {'geneName': 'PITX2',
'entrezGeneIds': [{'entrezGeneId': '5308'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000164093'}]},
'location': {'chromosomeName': '4',
'chromosomePosition': 110737238,
'region': {'name': '4q25'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs75021220{?projection}',
'templated': True}}},
'source': 'NCBI',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': True,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs75021220{?projection}',
'templated': True}}}]}],
'loci': [{'haplotypeSnpCount': None,
'description': 'Single variant',
'strongestRiskAlleles': [{'riskAlleleName': 'rs75021220-?',
'riskFrequency': None,
'genomeWide': False,
'limitedList': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs75021220{?projection}',
'templated': True}}}],
'authorReportedGenes': [{'geneName': 'PITX2',
'entrezGeneIds': [{'entrezGeneId': '5308'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000164093'}]}]}],
'riskFrequency': None,
'study': {'initialSampleSize': '602 European ancestry cases, 80,613 European ancestry controls',
'gxe': False,
'gxg': False,
'snpCount': None,
'qualifier': None,
'imputed': True,
'pooled': False,
'studyDesignComment': None,
'accessionId': 'GCST003490',
'fullPvalueSet': False,
'userRequested': False,
'platforms': [{'manufacturer': 'Affymetrix'}, {'manufacturer': 'Illumina'}],
'ancestries': [{'type': 'initial',
'numberOfIndividuals': 81215,
'ancestralGroups': [{'ancestralGroup': 'European'}],
'countryOfOrigin': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'Europe',
'region': 'Western Europe',
'countryName': 'France'},
{'majorArea': 'Europe',
'region': 'Western Europe',
'countryName': 'Netherlands'},
{'majorArea': 'Europe',
'region': 'Northern Europe',
'countryName': 'Sweden'},
{'majorArea': 'Northern America',
'region': None,
'countryName': 'U.S.'}]},
{'type': 'replication',
'numberOfIndividuals': 19094,
'ancestralGroups': [{'ancestralGroup': 'European'}],
'countryOfOrigin': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'Northern America',
'region': None,
'countryName': 'U.S.'},
{'majorArea': 'NR', 'region': 'NR', 'countryName': 'NR'}]},
{'type': 'replication',
'numberOfIndividuals': 35900,
'ancestralGroups': [{'ancestralGroup': 'European'},
{'ancestralGroup': 'Hispanic or Latin American'},
{'ancestralGroup': 'African unspecified'}],
'countryOfOrigin': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'Northern America',
'region': None,
'countryName': 'U.S.'},
{'majorArea': 'NR', 'region': 'NR', 'countryName': 'NR'}]}],
'diseaseTrait': {'trait': 'Ischemic stroke (cardioembolic)'},
'genotypingTechnologies': [{'genotypingTechnology': 'Genome-wide genotyping array'}],
'replicationSampleSize': '579 European ancestry cases, 3,427 African unspecified, Hispanic and European ancestry cases, 18,515 European ancestry controls, 32,473 African unspecified, Hispanic and European ancestry controls',
'publicationInfo': {'pubmedId': '27068588',
'publicationDate': '2016-04-07',
'publication': 'Lancet Neurol',
'title': 'Identification of additional risk loci for stroke and small vessel disease: a meta-analysis of genome-wide association studies.',
'author': {'fullname': 'Chauhan G', 'orcid': None}}},
'pvalueDescription': None,
'orPerCopyNum': 1.4,
'snpType': 'known',
'multiSnpHaplotype': False,
'snpInteraction': False,
'pvalueMantissa': 1,
'pvalueExponent': -25,
'standardError': None,
'pvalue': 1e-25,
'betaNum': None,
'betaUnit': None,
'betaDirection': None,
'description': None,
'_links': {'self': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/14964372'},
'association': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/14964372{?projection}',
'templated': True},
'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/14964372/snps'},
'backgroundEfoTraits': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/14964372/backgroundEfoTraits'},
'study': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/14964372/study'},
'efoTraits': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/14964372/efoTraits'}}},
{'range': '[1.11-1.23]',
'snps': [{'rsId': 'rs7193343',
'merged': 0,
'functionalClass': 'intron_variant',
'lastUpdateDate': '2021-06-24T18:30:01.511+0000',
'locations': [{'chromosomeName': '16',
'chromosomePosition': 72995261,
'region': {'name': '16q22.3'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7193343{?projection}',
'templated': True}}}],
'genomicContexts': [{'isIntergenic': False,
'isUpstream': False,
'isDownstream': False,
'distance': 0,
'gene': {'geneName': 'ZFHX3',
'entrezGeneIds': [{'entrezGeneId': '463'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000140836'}]},
'location': {'chromosomeName': '16',
'chromosomePosition': 72995261,
'region': {'name': '16q22.3'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7193343{?projection}',
'templated': True}}},
'source': 'Ensembl',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7193343{?projection}',
'templated': True}}},
{'isIntergenic': True,
'isUpstream': False,
'isDownstream': True,
'distance': 97088,
'gene': {'geneName': 'HCCAT5',
'entrezGeneIds': [{'entrezGeneId': '283902'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000260880'}]},
'location': {'chromosomeName': '16',
'chromosomePosition': 72995261,
'region': {'name': '16q22.3'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7193343{?projection}',
'templated': True}}},
'source': 'NCBI',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': True,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7193343{?projection}',
'templated': True}}},
{'isIntergenic': False,
'isUpstream': False,
'isDownstream': False,
'distance': 0,
'gene': {'geneName': 'ZFHX3',
'entrezGeneIds': [{'entrezGeneId': '463'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000140836'}]},
'location': {'chromosomeName': '16',
'chromosomePosition': 72995261,
'region': {'name': '16q22.3'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7193343{?projection}',
'templated': True}}},
'source': 'NCBI',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7193343{?projection}',
'templated': True}}},
{'isIntergenic': True,
'isUpstream': False,
'isDownstream': True,
'distance': 97088,
'gene': {'geneName': 'HCCAT5',
'entrezGeneIds': [{'entrezGeneId': '283902'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000260880'}]},
'location': {'chromosomeName': '16',
'chromosomePosition': 72995261,
'region': {'name': '16q22.3'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7193343{?projection}',
'templated': True}}},
'source': 'Ensembl',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': True,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7193343{?projection}',
'templated': True}}}]}],
'loci': [{'haplotypeSnpCount': None,
'description': 'Single variant',
'strongestRiskAlleles': [{'riskAlleleName': 'rs7193343-T',
'riskFrequency': '0.174',
'genomeWide': False,
'limitedList': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7193343{?projection}',
'templated': True},
'proxySnps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/NR{?projection}',
'templated': True}}}],
'authorReportedGenes': [{'geneName': 'ZFHX3',
'entrezGeneIds': [{'entrezGeneId': '463'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000140836'}]}]}],
'riskFrequency': '0.174',
'study': {'initialSampleSize': 'up to 3,400 European ancestry cases, up to 208 African ancestry cases, up to 118 Hispanic cases, 26,690 European ancestry controls, 3,354 African ancestry controls, 2,429 Hispanic controls',
'gxe': False,
'gxg': False,
'snpCount': 15400000,
'qualifier': 'up to',
'imputed': True,
'pooled': False,
'studyDesignComment': None,
'accessionId': 'GCST003257',
'fullPvalueSet': False,
'userRequested': False,
'platforms': [{'manufacturer': 'Illumina'}],
'ancestries': [{'type': 'initial',
'numberOfIndividuals': 30090,
'ancestralGroups': [{'ancestralGroup': 'European'}],
'countryOfOrigin': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'Europe',
'region': 'Northern Europe',
'countryName': 'Sweden'},
{'majorArea': 'Northern America', 'region': None, 'countryName': 'U.S.'},
{'majorArea': 'Europe',
'region': 'Eastern Europe',
'countryName': 'Poland'},
{'majorArea': 'Oceania',
'region': 'Australia/New Zealand',
'countryName': 'Australia'},
{'majorArea': 'Northern America',
'region': None,
'countryName': 'Canada'},
{'majorArea': 'Europe',
'region': 'Western Europe',
'countryName': 'Belgium'},
{'majorArea': 'Europe',
'region': 'Western Europe',
'countryName': 'Germany'},
{'majorArea': 'Europe',
'region': 'Northern Europe',
'countryName': 'U.K.'},
{'majorArea': 'Europe',
'region': 'Southern Europe',
'countryName': 'Spain'},
{'majorArea': 'Europe',
'region': 'Western Europe',
'countryName': 'Austria'},
{'majorArea': 'NR', 'region': 'NR', 'countryName': 'NR'}]},
{'type': 'replication',
'numberOfIndividuals': 3015,
'ancestralGroups': [{'ancestralGroup': 'African American or Afro-Caribbean'},
{'ancestralGroup': 'African unspecified'}],
'countryOfOrigin': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'Northern America',
'region': None,
'countryName': 'U.S.'},
{'majorArea': 'NR', 'region': 'NR', 'countryName': 'NR'}]},
{'type': 'replication',
'numberOfIndividuals': 360,
'ancestralGroups': [{'ancestralGroup': 'East Asian'}],
'countryOfOrigin': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}]},
{'type': 'replication',
'numberOfIndividuals': 277499,
'ancestralGroups': [{'ancestralGroup': 'European'}],
'countryOfOrigin': [{'majorArea': 'Europe',
'region': 'Northern Europe',
'countryName': 'Sweden'},
{'majorArea': 'Northern America', 'region': None, 'countryName': 'U.S.'},
{'majorArea': 'Europe',
'region': 'Northern Europe',
'countryName': 'Iceland'},
{'majorArea': 'Europe',
'region': 'Western Europe',
'countryName': 'Netherlands'},
{'majorArea': 'Europe',
'region': 'Western Europe',
'countryName': 'Germany'},
{'majorArea': 'Europe',
'region': 'Northern Europe',
'countryName': 'U.K.'},
{'majorArea': 'Europe',
'region': 'Southern Europe',
'countryName': 'Spain'},
{'majorArea': 'NR', 'region': 'NR', 'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}]},
{'type': 'replication',
'numberOfIndividuals': 783,
'ancestralGroups': [{'ancestralGroup': 'Hispanic or Latin American'}],
'countryOfOrigin': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}]},
{'type': 'replication',
'numberOfIndividuals': 5615,
'ancestralGroups': [{'ancestralGroup': 'South Asian'}],
'countryOfOrigin': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'Asia',
'region': 'South-central Asia',
'countryName': 'Pakistan'}]},
{'type': 'initial',
'numberOfIndividuals': 3562,
'ancestralGroups': [{'ancestralGroup': 'African American or Afro-Caribbean'},
{'ancestralGroup': 'African unspecified'}],
'countryOfOrigin': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'Northern America',
'region': None,
'countryName': 'U.S.'},
{'majorArea': 'NR', 'region': 'NR', 'countryName': 'NR'}]},
{'type': 'initial',
'numberOfIndividuals': 2547,
'ancestralGroups': [{'ancestralGroup': 'Hispanic or Latin American'}],
'countryOfOrigin': [{'majorArea': 'NR',
'region': 'NR',
'countryName': 'NR'}],
'countryOfRecruitment': [{'majorArea': 'Northern America',
'region': None,
'countryName': 'U.S.'}]}],
'diseaseTrait': {'trait': 'Ischemic stroke (cardioembolic)'},
'genotypingTechnologies': [{'genotypingTechnology': 'Genome-wide genotyping array'}],
'replicationSampleSize': '98 African ancestry cases, 31 East Asian ancestry cases, 3,436 European ancestry cases, 97 Hispanic cases, 422 South Asian ancestry cases, 2,917 African ancestry controls, 329 East Asian ancestry controls, 274,063 European ancestry controls, 686 Hispanic controls, 5,193 South Asian ancestry controls',
'publicationInfo': {'pubmedId': '26708676',
'publicationDate': '2015-12-18',
'publication': 'Lancet Neurol',
'title': 'Loci associated with ischaemic stroke and its subtypes (SiGN): a genome-wide association study.',
'author': {'fullname': 'Pulit SL', 'orcid': None}}},
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'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs139293840{?projection}',
'templated': True}}},
{'isIntergenic': False,
'isUpstream': False,
'isDownstream': False,
'distance': 0,
'gene': {'geneName': 'RPTOR',
'entrezGeneIds': [{'entrezGeneId': '57521'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000141564'}]},
'location': {'chromosomeName': '17',
'chromosomePosition': 80556559,
'region': {'name': '17q25.3'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs139293840{?projection}',
'templated': True}}},
'source': 'Ensembl',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs139293840{?projection}',
'templated': True}}},
{'isIntergenic': True,
'isUpstream': True,
'isDownstream': False,
'distance': 13904,
'gene': {'geneName': 'RPL32P31',
'entrezGeneIds': [{'entrezGeneId': '201259'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000213128'}]},
'location': {'chromosomeName': '17',
'chromosomePosition': 80556559,
'region': {'name': '17q25.3'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs139293840{?projection}',
'templated': True}}},
'source': 'NCBI',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': True,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs139293840{?projection}',
'templated': True}}},
{'isIntergenic': False,
'isUpstream': False,
'isDownstream': False,
'distance': 0,
'gene': {'geneName': 'RPTOR',
'entrezGeneIds': [{'entrezGeneId': '57521'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000141564'}]},
'location': {'chromosomeName': '17',
'chromosomePosition': 80556559,
'region': {'name': '17q25.3'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs139293840{?projection}',
'templated': True}}},
'source': 'NCBI',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs139293840{?projection}',
'templated': True}}},
{'isIntergenic': True,
'isUpstream': True,
'isDownstream': False,
'distance': 13954,
'gene': {'geneName': 'RPL32P31',
'entrezGeneIds': [{'entrezGeneId': '201259'}],
'ensemblGeneIds': [{'ensemblGeneId': 'ENSG00000213128'}]},
'location': {'chromosomeName': '17',
'chromosomePosition': 80556559,
'region': {'name': '17q25.3'},
'_links': {'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs139293840{?projection}',
'templated': True}}},
'source': 'Ensembl',
'mappingMethod': 'Ensembl_pipeline',
'isClosestGene': True,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs139293840{?projection}',
'templated': True}}}]}],
'loci': [{'haplotypeSnpCount': None,
'description': 'Single variant',
'strongestRiskAlleles': [{'riskAlleleName': 'rs139293840-A',
'riskFrequency': None,
'genomeWide': False,
'limitedList': False,
'_links': {'snp': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs139293840{?projection}',
'templated': True}}}],
'authorReportedGenes': []}],
'riskFrequency': '0.01013',
'study': {'initialSampleSize': '497 Korean ancestry cases, 4,815 Korean ancestry controls',
'gxe': False,
'gxg': False,
'snpCount': 7975321,
'qualifier': None,
'imputed': True,
'pooled': False,
'studyDesignComment': None,
'accessionId': 'GCST90014127',
'fullPvalueSet': False,
'userRequested': False,
'platforms': [{'manufacturer': 'Affymetrix'}],
'ancestries': [{'type': 'initial',
'numberOfIndividuals': 5312,
'ancestralGroups': [{'ancestralGroup': 'East Asian'}],
'countryOfOrigin': [],
'countryOfRecruitment': [{'majorArea': 'Asia',
'region': 'Eastern Asia',
'countryName': 'Republic of Korea'}]}],
'diseaseTrait': {'trait': 'Cardio-cerebrovascular disease in diabetes mellitus'},
'genotypingTechnologies': [{'genotypingTechnology': 'Genome-wide genotyping array'}],
'replicationSampleSize': 'NA',
'publicationInfo': {'pubmedId': '33632238',
'publicationDate': '2021-02-25',
'publication': 'J Transl Med',
'title': 'Identification of susceptibility loci for cardiovascular disease in adults with hypertension, diabetes, and dyslipidemia.',
'author': {'fullname': 'Song Y', 'orcid': None}}},
'pvalueDescription': None,
'orPerCopyNum': 3.511,
'snpType': 'novel',
'multiSnpHaplotype': False,
'snpInteraction': False,
'pvalueMantissa': 2,
'pvalueExponent': -8,
'standardError': None,
'pvalue': 2e-08,
'betaNum': None,
'betaUnit': None,
'betaDirection': None,
'description': None,
'_links': {'self': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/78852290'},
'association': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/78852290{?projection}',
'templated': True},
'snps': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/78852290/snps'},
'backgroundEfoTraits': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/78852290/backgroundEfoTraits'},
'study': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/78852290/study'},
'efoTraits': {'href': 'https://www.ebi.ac.uk/gwas/rest/api/associations/78852290/efoTraits'}}}]
In [3]:
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def gwascatalog_trait(efo):
base_url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/"+efo+"/associations?projection=associationByEfoTrait"
print("Start to retrieve data from GWASCatalog...")
print(" -Requesting GWAS associations through the following GWASCatalog API...")
print(" -api: "+ base_url)
# Perform POST request and check status code of response
r = requests.get(base_url)
if r.status_code == 200:
print(" -Retrieved data from GWASCatalog successffully ...")
print(" -Loading json ...")
# Transform API response from JSON into Python dictionary and print in console
api_response = json.loads(r.text)
records=[]
sig_level = 5e-8
print(" -Parsing json ...")
records=list()
source="NCBI"
print("Number of associations:",len( api_response["_embedded"]["associations"]))
for association in api_response["_embedded"]["associations"]:
#association statistics:
p=float(association["pvalue"])
if p < sig_level:
try:
function_class=association["functionalClass"]
except:
function_class=None
try:
eaf=association["riskFrequency"]
except:
eaf= None
try:
study=association["study"]['publicationInfo']["title"]
pubmedid=association["study"]['publicationInfo']["pubmedId"]
except:
study= None
pubmedid = None
try:
gene = association["loci"][0]["authorReportedGenes"][0]["geneName"]
except:
gene = None
try:
OR=association["orPerCopyNum"]
except:
OR=None
try:
beta=association["betaNum"]
se=association["standardError"]
except:
beta=None
se=None
for snp in association["snps"]:
if len(snp["locations"])>0:
for record_num in range(len(snp["locations"])):
if snp["locations"][record_num]["chromosomeName"] in [str(i) for i in range(1,26)]+["x","X","y","Y","mt","MT"]:
if len(snp["genomicContexts"])>0:
closegenes=set()
distances=set()
ingene=set()
for gene_num in range(len(snp["genomicContexts"])):
if snp["genomicContexts"][gene_num]["source"]==source:
distance= str(snp["genomicContexts"][gene_num]["distance"])
ingene_name = snp["genomicContexts"][gene_num]["gene"]["geneName"]
if distance==0:
ingene.add(ingene_name)
continue
if snp["genomicContexts"][gene_num]["isClosestGene"] is True:
closegene_name = snp["genomicContexts"][gene_num]["gene"]["geneName"]
closegenes.add(closegene_name)
distances.add(distance)
if len(ingene)>0:
autogenes = ",".ingene
else:
autogenes = ",".join(closegenes)
row=[ snp["rsId"],
snp["locations"][record_num]["chromosomeName"],
snp["locations"][record_num]["chromosomePosition"],
gene,
autogenes,
function_class,
OR,
beta,
se,
p,
association["study"]["diseaseTrait"]["trait"],
study,
pubmedid
]
records.append(row)
#rsid locations
gwascatalog = pd.DataFrame(records,columns=["SNPID","CHR","POS","REPORT_GENENAME","CLOSEST_GENENAMES","FUNCTION_CLASS","OR","BETA","SE","P","TRAIT","STUDY","PUBMEDID"])
sigs = gl.Sumstats(gwascatalog,fmt="gwaslab",other=['REPORT_GENENAME', 'CLOSEST_GENENAMES','TRAIT', 'STUDY', 'PUBMEDID'],verbose=False)
sigs.fix_pos(verbose=False)
sigs.fix_chr(verbose=False)
sigs.sort_coordinate()
return sigs.data
def gwascatalog_trait(efo):
base_url = "https://www.ebi.ac.uk/gwas/rest/api/efoTraits/"+efo+"/associations?projection=associationByEfoTrait"
print("Start to retrieve data from GWASCatalog...")
print(" -Requesting GWAS associations through the following GWASCatalog API...")
print(" -api: "+ base_url)
# Perform POST request and check status code of response
r = requests.get(base_url)
if r.status_code == 200:
print(" -Retrieved data from GWASCatalog successffully ...")
print(" -Loading json ...")
# Transform API response from JSON into Python dictionary and print in console
api_response = json.loads(r.text)
records=[]
sig_level = 5e-8
print(" -Parsing json ...")
records=list()
source="NCBI"
print("Number of associations:",len( api_response["_embedded"]["associations"]))
for association in api_response["_embedded"]["associations"]:
#association statistics:
p=float(association["pvalue"])
if p < sig_level:
try:
function_class=association["functionalClass"]
except:
function_class=None
try:
eaf=association["riskFrequency"]
except:
eaf= None
try:
study=association["study"]['publicationInfo']["title"]
pubmedid=association["study"]['publicationInfo']["pubmedId"]
except:
study= None
pubmedid = None
try:
gene = association["loci"][0]["authorReportedGenes"][0]["geneName"]
except:
gene = None
try:
OR=association["orPerCopyNum"]
except:
OR=None
try:
beta=association["betaNum"]
se=association["standardError"]
except:
beta=None
se=None
for snp in association["snps"]:
if len(snp["locations"])>0:
for record_num in range(len(snp["locations"])):
if snp["locations"][record_num]["chromosomeName"] in [str(i) for i in range(1,26)]+["x","X","y","Y","mt","MT"]:
if len(snp["genomicContexts"])>0:
closegenes=set()
distances=set()
ingene=set()
for gene_num in range(len(snp["genomicContexts"])):
if snp["genomicContexts"][gene_num]["source"]==source:
distance= str(snp["genomicContexts"][gene_num]["distance"])
ingene_name = snp["genomicContexts"][gene_num]["gene"]["geneName"]
if distance==0:
ingene.add(ingene_name)
continue
if snp["genomicContexts"][gene_num]["isClosestGene"] is True:
closegene_name = snp["genomicContexts"][gene_num]["gene"]["geneName"]
closegenes.add(closegene_name)
distances.add(distance)
if len(ingene)>0:
autogenes = ",".ingene
else:
autogenes = ",".join(closegenes)
row=[ snp["rsId"],
snp["locations"][record_num]["chromosomeName"],
snp["locations"][record_num]["chromosomePosition"],
gene,
autogenes,
function_class,
OR,
beta,
se,
p,
association["study"]["diseaseTrait"]["trait"],
study,
pubmedid
]
records.append(row)
#rsid locations
gwascatalog = pd.DataFrame(records,columns=["SNPID","CHR","POS","REPORT_GENENAME","CLOSEST_GENENAMES","FUNCTION_CLASS","OR","BETA","SE","P","TRAIT","STUDY","PUBMEDID"])
sigs = gl.Sumstats(gwascatalog,fmt="gwaslab",other=['REPORT_GENENAME', 'CLOSEST_GENENAMES','TRAIT', 'STUDY', 'PUBMEDID'],verbose=False)
sigs.fix_pos(verbose=False)
sigs.fix_chr(verbose=False)
sigs.sort_coordinate()
return sigs.data
In [21]:
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known = gwascatalog_trait(efo="HP_0002140")
known = gwascatalog_trait(efo="HP_0002140")
Start to retrieve data from GWASCatalog... -Requesting GWAS associations through the following GWASCatalog API... -api: https://www.ebi.ac.uk/gwas/rest/api/efoTraits/HP_0002140/associations?projection=associationByEfoTrait -Retrieved data from GWASCatalog successffully ... -Loading json ... -Parsing json ... Number of associations: 429 Thu Oct 27 00:13:50 2022 Start to sort the genome coordinates... Thu Oct 27 00:13:50 2022 -Current Dataframe shape : 198 x 13 Thu Oct 27 00:13:50 2022 -Force converting POS to integers... Thu Oct 27 00:13:50 2022 -Sorting genome coordinates... Thu Oct 27 00:13:50 2022 Finished sorting genome coordinates successfully!
In [5]:
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known
known
Out[5]:
| SNPID | CHR | POS | BETA | SE | P | OR | STATUS | REPORT_GENENAME | CLOSEST_GENENAMES | TRAIT | STUDY | PUBMEDID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | rs880315 | 1 | 10736809 | NaN | NaN | 6.000000e-09 | NaN | 9990999 | CASZ1 | PEX14,LOC105376733 | Ischemic stroke | Multiancestry genome-wide association study of... | 29531354 |
| 1 | rs491203 | 1 | 15102737 | NaN | NaN | 8.000000e-09 | NaN | 9990999 | TMEM51 | TMEM51-AS1 | Acute ischemic stroke x type 2 diabetes intera... | Discovery of 318 new risk loci for type 2 diab... | 32541925 |
| 2 | rs138364069 | 1 | 50832533 | NaN | NaN | 7.000000e-09 | 3.570 | 9990999 | FAF1 | MRPS6P2,PHBP12 | Ischemic stroke (large artery atherosclerosis) | Genome-Wide Association Analysis of Young-Onse... | 26732560 |
| 3 | rs3176471 | 1 | 50973784 | NaN | NaN | 4.000000e-08 | 3.700 | 9990999 | CDKN2C | FAF1,MIR4421 | Ischemic stroke (large artery atherosclerosis) | Genome-Wide Association Analysis of Young-Onse... | 26732560 |
| 4 | rs1892534 | 1 | 65640261 | NaN | NaN | 2.000000e-16 | NaN | 9990999 | None | RN7SL854P,LOC100422694 | Ischemic stroke or fibrinogen levels (pleiotropy) | Multi-phenotype analyses of hemostatic traits ... | 35285134 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 193 | rs1800961 | 20 | 44413724 | NaN | NaN | 1.000000e-10 | NaN | 9990999 | None | MIR3646,LINC01430 | Ischemic stroke or fibrinogen levels (pleiotropy) | Multi-phenotype analyses of hemostatic traits ... | 35285134 |
| 194 | rs9808651 | 21 | 39094542 | NaN | NaN | 6.000000e-22 | NaN | 9990999 | None | RPL23AP12,LOC107985484 | Ischemic stroke or fibrinogen levels (pleiotropy) | Multi-phenotype analyses of hemostatic traits ... | 35285134 |
| 195 | rs5750823 | 22 | 39433968 | NaN | NaN | 1.000000e-12 | NaN | 9990999 | None | SYNGR1,MGAT3 | Ischemic stroke or von Willebrand factor level... | Multi-phenotype analyses of hemostatic traits ... | 35285134 |
| 196 | rs75347843 | 22 | 50673933 | NaN | NaN | 6.000000e-11 | NaN | 9990999 | None | ARSA,RNU6-409P | Ischemic stroke or fibrinogen levels (pleiotropy) | Multi-phenotype analyses of hemostatic traits ... | 35285134 |
| 197 | rs114209171 | 23 | 155050522 | NaN | NaN | 7.000000e-13 | 1.153 | 9990999 | F8 | F8,CMC4 | Thrombosis | Genome-wide association analysis of self-repor... | 26908601 |
198 rows × 13 columns
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!wget -O t2d_bbj.txt.gz http://jenger.riken.jp/14/
!wget -O t2d_bbj.txt.gz http://jenger.riken.jp/14/
--2022-10-26 23:54:33-- http://jenger.riken.jp/14/ Resolving jenger.riken.jp (jenger.riken.jp)... 134.160.84.25 Connecting to jenger.riken.jp (jenger.riken.jp)|134.160.84.25|:80... connected. HTTP request sent, awaiting response... 200 OK Length: 274187574 (261M) [text/plain] Saving to: ‘t2d_bbj.txt.gz’ t2d_bbj.txt.gz 100%[===================>] 261.49M 11.1MB/s in 24s 2022-10-26 23:55:24 (10.8 MB/s) - ‘t2d_bbj.txt.gz’ saved [274187574/274187574]
In [4]:
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Thu Oct 27 00:58:00 2022 Start to initiate from file :t2d_bbj.txt.gz Thu Oct 27 00:58:01 2022 -Reading columns : POS,BETA,N,REF,P,ALT,SE,Frq,Dir,SNP,CHR Thu Oct 27 00:58:01 2022 -Renaming columns to : POS,BETA,N,NEA,P,EA,SE,EAF,DIRECTION,SNPID,CHR Thu Oct 27 00:58:01 2022 -Current dataframe shape : Rows 1000000 x 11 Columns Thu Oct 27 00:58:01 2022 -Initiating a status column ... Thu Oct 27 00:58:01 2022 -Reordering columns to : SNPID,CHR,POS,EA,NEA,EAF,BETA,SE,P,N,DIRECTION,STATUS Thu Oct 27 00:58:01 2022 -NEAF is specified... Thu Oct 27 00:58:01 2022 -Checking if 0<= NEAF <=1 ... Thu Oct 27 00:58:01 2022 -Converted NEAF to EAF. Thu Oct 27 00:58:01 2022 -Removed 0 variants with bad NEAF. Thu Oct 27 00:58:01 2022 Finished loading data successfully!
In [5]:
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Thu Oct 27 00:58:01 2022 Start to check IDs... Thu Oct 27 00:58:01 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 00:58:01 2022 -Checking if SNPID is chr:pos:ref:alt...(separator: - ,: , _) Thu Oct 27 00:58:03 2022 Finished checking IDs successfully! Thu Oct 27 00:58:03 2022 Start to fix chromosome notation... Thu Oct 27 00:58:03 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 00:58:05 2022 -All CHR are already fixed... Thu Oct 27 00:58:06 2022 Finished fixing chromosome notation successfully! Thu Oct 27 00:58:06 2022 Start to fix basepair positions... Thu Oct 27 00:58:06 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 00:58:07 2022 -Position upper_bound is: 250,000,000 Thu Oct 27 00:58:09 2022 -Remove outliers: 0 Thu Oct 27 00:58:09 2022 -Converted all position to datatype Int64. Thu Oct 27 00:58:09 2022 Finished fixing basepair position successfully! Thu Oct 27 00:58:09 2022 Start to fix alleles... Thu Oct 27 00:58:09 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 00:58:10 2022 -Converted all bases to string datatype and UPPERCASE. Thu Oct 27 00:58:11 2022 Finished fixing allele successfully! Thu Oct 27 00:58:11 2022 Start sanity check for statistics ... Thu Oct 27 00:58:11 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 00:58:11 2022 -Checking if N is Int64 and N>0 ... Thu Oct 27 00:58:11 2022 -Removed 0 variants with bad N. Thu Oct 27 00:58:11 2022 -Checking if 0<= EAF <=1 ... Thu Oct 27 00:58:11 2022 -Removed 0 variants with bad EAF. Thu Oct 27 00:58:11 2022 -Checking if MAC >=5 ... Thu Oct 27 00:58:11 2022 -Removed 0 variants with bad MAC. Thu Oct 27 00:58:11 2022 -Checking if 0< P <5e-300 ... Thu Oct 27 00:58:11 2022 -Removed 0 variants with bad P. Thu Oct 27 00:58:11 2022 -Checking if abs(BETA)<10 ... Thu Oct 27 00:58:11 2022 -Removed 0 variants with bad BETA. Thu Oct 27 00:58:11 2022 -Checking if SE >0 ... Thu Oct 27 00:58:11 2022 -Removed 0 variants with bad SE. Thu Oct 27 00:58:11 2022 -Checking STATUS... Thu Oct 27 00:58:12 2022 -Coverting STAUTUS to category. Thu Oct 27 00:58:12 2022 -Removed 0 variants with bad statistics in total. Thu Oct 27 00:58:12 2022 Finished sanity check successfully! Thu Oct 27 00:58:12 2022 Start to normalize variants... Thu Oct 27 00:58:12 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 00:58:12 2022 -No available variants to normalize.. Thu Oct 27 00:58:12 2022 Finished normalizing variants successfully!
In [6]:
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mysumstats.liftover(from_build="19",to_build="38",n_cores=4)
mysumstats.liftover(from_build="19",to_build="38",n_cores=4)
Thu Oct 27 00:58:12 2022 Start to perform liftover... Thu Oct 27 00:58:12 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 00:58:12 2022 -CPU Cores to use : 4 Thu Oct 27 00:58:12 2022 -Performing liftover ... Thu Oct 27 00:58:12 2022 -Creating converter : hg19 to hg38 Thu Oct 27 00:58:12 2022 -Converting variants with status code xxx0xxx :1000000... Thu Oct 27 00:59:23 2022 -Removed unmapped variants: 619 Thu Oct 27 00:59:28 2022 Finished liftover successfully!
In [7]:
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mysumstats.data
mysumstats.data
Out[7]:
| SNPID | CHR | POS | EA | NEA | EAF | BETA | SE | P | N | DIRECTION | STATUS | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1:725932_G_A | 1 | 790552 | G | A | 0.9960 | -0.0737 | 0.1394 | 0.59700 | 166718 | -?+- | 3860099 |
| 1 | 1:725933_A_G | 1 | 790553 | G | A | 0.0040 | 0.0737 | 0.1394 | 0.59730 | 166718 | +?-+ | 3860099 |
| 2 | 1:737801_T_C | 1 | 802421 | C | T | 0.0051 | 0.0490 | 0.1231 | 0.69080 | 166718 | +?-+ | 3860099 |
| 3 | 1:749963_T_TAA | 1 | 814583 | TAA | T | 0.8374 | 0.0213 | 0.0199 | 0.28460 | 166718 | -?++ | 3860399 |
| 4 | 1:751343_T_A | 1 | 815963 | T | A | 0.8593 | 0.0172 | 0.0156 | 0.27050 | 166718 | -?++ | 3860099 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 999995 | 2:6347639_C_A | 2 | 6207507 | C | A | 0.7900 | -0.0159 | 0.0111 | 0.15100 | 191764 | ---+ | 3860099 |
| 999996 | 2:6347694_G_C | 2 | 6207562 | G | C | 0.7907 | -0.0151 | 0.0111 | 0.17250 | 191764 | ---+ | 3860099 |
| 999997 | 2:6348478_G_A | 2 | 6208346 | G | A | 0.7860 | -0.0152 | 0.0111 | 0.17160 | 191764 | ---+ | 3860099 |
| 999998 | 2:6348490_G_C | 2 | 6208358 | G | C | 0.9985 | -0.3180 | 0.2032 | 0.11750 | 191764 | +--- | 3860099 |
| 999999 | 2:6348754_A_C | 2 | 6208622 | C | A | 0.1572 | 0.0297 | 0.0126 | 0.01846 | 191764 | +++- | 3860099 |
999381 rows × 12 columns
In [23]:
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known = gwascatalog_trait(efo="MONDO_0005148")
known = gwascatalog_trait(efo="MONDO_0005148")
Start to retrieve data from GWASCatalog... -Requesting GWAS associations through the following GWASCatalog API... -api: https://www.ebi.ac.uk/gwas/rest/api/efoTraits/MONDO_0005148/associations?projection=associationByEfoTrait -Retrieved data from GWASCatalog successffully ... -Loading json ... -Parsing json ... Number of associations: 5253 Thu Oct 27 00:35:03 2022 Start to sort the genome coordinates... Thu Oct 27 00:35:03 2022 -Current Dataframe shape : 3939 x 13 Thu Oct 27 00:35:03 2022 -Force converting POS to integers... Thu Oct 27 00:35:03 2022 -Sorting genome coordinates... Thu Oct 27 00:35:03 2022 Finished sorting genome coordinates successfully!
In [24]:
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known
known
Out[24]:
| SNPID | CHR | POS | BETA | SE | P | OR | STATUS | REPORT_GENENAME | CLOSEST_GENENAMES | TRAIT | STUDY | PUBMEDID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | rs12741141 | 1 | 6609910 | 0.0395 | 0.0048 | 1.000000e-16 | NaN | 9990999 | NR | KLHL21,PHF13 | Type 2 diabetes | Discovery of 318 new risk loci for type 2 diab... | 32541925 |
| 1 | rs11583755 | 1 | 6612669 | 0.0369 | 0.0041 | 1.000000e-19 | NaN | 9990999 | NR | KLHL21,PHF13 | Type 2 diabetes | Discovery of 318 new risk loci for type 2 diab... | 32541925 |
| 2 | rs1010447 | 1 | 11209739 | NaN | NaN | 2.000000e-08 | NaN | 9990999 | MTOR | RNU6-291P,ANGPTL7 | Body mass index and type 2 diabetes (pairwise) | Genome-wide discovery of genetic loci that unc... | 33619380 |
| 3 | rs4845987 | 1 | 11246222 | 0.0282 | 0.0051 | 3.000000e-08 | NaN | 9990999 | NR | RPL39P6,UBIAD1 | Type 2 diabetes | Discovery of 318 new risk loci for type 2 diab... | 32541925 |
| 4 | rs7554251 | 1 | 11257875 | 0.0297 | 0.0045 | 3.000000e-11 | NaN | 9990999 | NR | RPL39P6,UBIAD1 | Type 2 diabetes | Discovery of 318 new risk loci for type 2 diab... | 32541925 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3934 | rs5945326 | 23 | 153634467 | NaN | NaN | 2.000000e-12 | 1.140000 | 9990999 | DUSP9 | LOC105373384,DUSP9 | Type 2 diabetes | Genome-wide association study identifies three... | 23945395 |
| 3935 | rs5945326 | 23 | 153634467 | NaN | NaN | 3.000000e-10 | 1.270000 | 9990999 | DUSP9 | LOC105373384,DUSP9 | Type 2 diabetes | Twelve type 2 diabetes susceptibility loci ide... | 20581827 |
| 3936 | rs5945326 | 23 | 153634467 | 0.1203 | 0.0055 | 8.000000e-106 | NaN | 9990999 | None | LOC105373384,DUSP9 | Type 2 diabetes | A cross-population atlas of genetic associatio... | 34594039 |
| 3937 | rs1894299 | 23 | 153643433 | NaN | NaN | 9.000000e-58 | 1.130000 | 9990999 | DUSP9 | LOC105373384,RPL18AP16 | Type 2 diabetes | Identification of 28 new susceptibility loci f... | 30718926 |
| 3938 | rs1894299 | 23 | 153643433 | NaN | NaN | 2.000000e-67 | 0.884338 | 9990999 | None | LOC105373384,RPL18AP16 | Type 2 diabetes | Large-scale genome-wide association study in a... | 32514122 |
3939 rows × 13 columns
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known.to_csv("t2d_known.txt",index=None)
known.to_csv("t2d_known.txt",index=None)
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import requests
import json
import pandas as pd
import sys
sys.path.insert(0,"/home/yunye/gwaslab/gwaslab/src")
import gwaslab as gl
import requests
import json
import pandas as pd
import sys
sys.path.insert(0,"/home/yunye/gwaslab/gwaslab/src")
import gwaslab as gl
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mysumstats = gl.Sumstats("t2d_bbj.txt.gz",
snpid="SNP",
chrom="CHR",
pos="POS",
ea="ALT",
nea="REF",
neaf="Frq",
beta="BETA",
se="SE",
p="P",
direction="Dir",
n="N",nrows=1000000)
mysumstats = gl.Sumstats("t2d_bbj.txt.gz",
snpid="SNP",
chrom="CHR",
pos="POS",
ea="ALT",
nea="REF",
neaf="Frq",
beta="BETA",
se="SE",
p="P",
direction="Dir",
n="N",nrows=1000000)
Thu Oct 27 01:33:16 2022 Start to initiate from file :t2d_bbj.txt.gz Thu Oct 27 01:33:17 2022 -Reading columns : CHR,REF,P,POS,SE,BETA,ALT,SNP,Dir,Frq,N Thu Oct 27 01:33:17 2022 -Renaming columns to : CHR,NEA,P,POS,SE,BETA,EA,SNPID,DIRECTION,EAF,N Thu Oct 27 01:33:17 2022 -Current dataframe shape : Rows 1000000 x 11 Columns Thu Oct 27 01:33:17 2022 -Initiating a status column ... Thu Oct 27 01:33:17 2022 -Reordering columns to : SNPID,CHR,POS,EA,NEA,EAF,BETA,SE,P,N,DIRECTION,STATUS Thu Oct 27 01:33:18 2022 -NEAF is specified... Thu Oct 27 01:33:18 2022 -Checking if 0<= NEAF <=1 ... Thu Oct 27 01:33:18 2022 -Converted NEAF to EAF. Thu Oct 27 01:33:18 2022 -Removed 0 variants with bad NEAF. Thu Oct 27 01:33:18 2022 Finished loading data successfully!
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mysumstats.basic_check()
mysumstats.basic_check()
Thu Oct 27 01:33:18 2022 Start to check IDs... Thu Oct 27 01:33:18 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 01:33:18 2022 -Checking if SNPID is chr:pos:ref:alt...(separator: - ,: , _) Thu Oct 27 01:33:19 2022 Finished checking IDs successfully! Thu Oct 27 01:33:19 2022 Start to fix chromosome notation... Thu Oct 27 01:33:19 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 01:33:21 2022 -All CHR are already fixed... Thu Oct 27 01:33:23 2022 Finished fixing chromosome notation successfully! Thu Oct 27 01:33:23 2022 Start to fix basepair positions... Thu Oct 27 01:33:23 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 01:33:24 2022 -Position upper_bound is: 250,000,000 Thu Oct 27 01:33:25 2022 -Remove outliers: 0 Thu Oct 27 01:33:26 2022 -Converted all position to datatype Int64. Thu Oct 27 01:33:26 2022 Finished fixing basepair position successfully! Thu Oct 27 01:33:26 2022 Start to fix alleles... Thu Oct 27 01:33:26 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 01:33:26 2022 -Converted all bases to string datatype and UPPERCASE. Thu Oct 27 01:33:27 2022 Finished fixing allele successfully! Thu Oct 27 01:33:27 2022 Start sanity check for statistics ... Thu Oct 27 01:33:27 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 01:33:27 2022 -Checking if N is Int64 and N>0 ... Thu Oct 27 01:33:27 2022 -Removed 0 variants with bad N. Thu Oct 27 01:33:27 2022 -Checking if 0<= EAF <=1 ... Thu Oct 27 01:33:27 2022 -Removed 0 variants with bad EAF. Thu Oct 27 01:33:27 2022 -Checking if MAC >=5 ... Thu Oct 27 01:33:27 2022 -Removed 0 variants with bad MAC. Thu Oct 27 01:33:27 2022 -Checking if 0< P <5e-300 ... Thu Oct 27 01:33:28 2022 -Removed 0 variants with bad P. Thu Oct 27 01:33:28 2022 -Checking if abs(BETA)<10 ... Thu Oct 27 01:33:28 2022 -Removed 0 variants with bad BETA. Thu Oct 27 01:33:28 2022 -Checking if SE >0 ... Thu Oct 27 01:33:28 2022 -Removed 0 variants with bad SE. Thu Oct 27 01:33:28 2022 -Checking STATUS... Thu Oct 27 01:33:28 2022 -Coverting STAUTUS to category. Thu Oct 27 01:33:28 2022 -Removed 0 variants with bad statistics in total. Thu Oct 27 01:33:28 2022 Finished sanity check successfully! Thu Oct 27 01:33:28 2022 Start to normalize variants... Thu Oct 27 01:33:28 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 01:33:29 2022 -No available variants to normalize.. Thu Oct 27 01:33:29 2022 Finished normalizing variants successfully!
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mysumstats.liftover(from_build="19",to_build="38",n_cores=4)
mysumstats.liftover(from_build="19",to_build="38",n_cores=4)
Thu Oct 27 01:34:28 2022 Start to perform liftover... Thu Oct 27 01:34:28 2022 -Current Dataframe shape : 1000000 x 12 Thu Oct 27 01:34:28 2022 -CPU Cores to use : 4 Thu Oct 27 01:34:28 2022 -Performing liftover ... Thu Oct 27 01:34:28 2022 -Creating converter : hg19 to hg38 Thu Oct 27 01:34:29 2022 -Converting variants with status code xxx0xxx :1000000... Thu Oct 27 01:35:39 2022 -Removed unmapped variants: 619 Thu Oct 27 01:35:45 2022 Finished liftover successfully!
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known = pd.read_csv("t2d_known.txt")
known = pd.read_csv("t2d_known.txt")
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known
known
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| SNPID | CHR | POS | BETA | SE | P | OR | STATUS | REPORT_GENENAME | CLOSEST_GENENAMES | TRAIT | STUDY | PUBMEDID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | rs12741141 | 1 | 6609910 | 0.0395 | 0.0048 | 1.000000e-16 | NaN | 9990999 | NR | KLHL21,PHF13 | Type 2 diabetes | Discovery of 318 new risk loci for type 2 diab... | 32541925 |
| 1 | rs11583755 | 1 | 6612669 | 0.0369 | 0.0041 | 1.000000e-19 | NaN | 9990999 | NR | KLHL21,PHF13 | Type 2 diabetes | Discovery of 318 new risk loci for type 2 diab... | 32541925 |
| 2 | rs1010447 | 1 | 11209739 | NaN | NaN | 2.000000e-08 | NaN | 9990999 | MTOR | RNU6-291P,ANGPTL7 | Body mass index and type 2 diabetes (pairwise) | Genome-wide discovery of genetic loci that unc... | 33619380 |
| 3 | rs4845987 | 1 | 11246222 | 0.0282 | 0.0051 | 3.000000e-08 | NaN | 9990999 | NR | RPL39P6,UBIAD1 | Type 2 diabetes | Discovery of 318 new risk loci for type 2 diab... | 32541925 |
| 4 | rs7554251 | 1 | 11257875 | 0.0297 | 0.0045 | 3.000000e-11 | NaN | 9990999 | NR | RPL39P6,UBIAD1 | Type 2 diabetes | Discovery of 318 new risk loci for type 2 diab... | 32541925 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3934 | rs5945326 | 23 | 153634467 | NaN | NaN | 2.000000e-12 | 1.140000 | 9990999 | DUSP9 | LOC105373384,DUSP9 | Type 2 diabetes | Genome-wide association study identifies three... | 23945395 |
| 3935 | rs5945326 | 23 | 153634467 | NaN | NaN | 3.000000e-10 | 1.270000 | 9990999 | DUSP9 | LOC105373384,DUSP9 | Type 2 diabetes | Twelve type 2 diabetes susceptibility loci ide... | 20581827 |
| 3936 | rs5945326 | 23 | 153634467 | 0.1203 | 0.0055 | 8.000000e-106 | NaN | 9990999 | NaN | LOC105373384,DUSP9 | Type 2 diabetes | A cross-population atlas of genetic associatio... | 34594039 |
| 3937 | rs1894299 | 23 | 153643433 | NaN | NaN | 9.000000e-58 | 1.130000 | 9990999 | DUSP9 | LOC105373384,RPL18AP16 | Type 2 diabetes | Identification of 28 new susceptibility loci f... | 30718926 |
| 3938 | rs1894299 | 23 | 153643433 | NaN | NaN | 2.000000e-67 | 0.884338 | 9990999 | NaN | LOC105373384,RPL18AP16 | Type 2 diabetes | Large-scale genome-wide association study in a... | 32514122 |
3939 rows × 13 columns
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mysumstats.get_novel(known = known,only_novel=False)
mysumstats.get_novel(known = known,only_novel=False)
Thu Oct 27 01:35:53 2022 Start to extract lead variants... Thu Oct 27 01:35:53 2022 -Processing 999381 variants... Thu Oct 27 01:35:53 2022 -Significance threshold : 5e-08 Thu Oct 27 01:35:53 2022 -Sliding window size: 500 kb Thu Oct 27 01:35:54 2022 -Found 543 significant variants in total... Thu Oct 27 01:35:54 2022 -Identified 4 lead variants! Thu Oct 27 01:35:54 2022 Finished extracting lead variants successfully! Thu Oct 27 01:35:54 2022 Start to check if lead variants are known... Thu Oct 27 01:35:54 2022 -Lead variants in known loci: 3939 Thu Oct 27 01:35:54 2022 -Checking the minimum distance between identified lead variants and provided known variants... Thu Oct 27 01:35:54 2022 -Identified 4 known vairants in current sumstats... Thu Oct 27 01:35:54 2022 -Identified 0 novel vairants in current sumstats... Thu Oct 27 01:35:54 2022 Finished checking known or novel successfully!
Out[9]:
| SNPID | CHR | POS | EA | NEA | EAF | BETA | SE | P | N | DIRECTION | STATUS | TCHR+POS | DISTANCE_TO_KNOWN | KNOWN_ID | NOVEL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 96739 | 1:22068326_A_G | 1 | 21741833 | G | A | 0.7550 | 0.0621 | 0.0103 | 1.629000e-09 | 191764 | ++++ | 3860099 | 1021741833 | 0 | rs1825307 | False |
| 213860 | 1:51103268_T_C | 1 | 50637596 | C | T | 0.7953 | -0.0802 | 0.0120 | 2.519000e-11 | 191764 | ---- | 3860099 | 1050637596 | 0 | rs12031188 | False |
| 534095 | 1:154309595_TA_T | 1 | 154337119 | TA | T | 0.0947 | -0.0915 | 0.0166 | 3.289000e-08 | 191764 | ---- | 3860399 | 1154337119 | 1 | rs68062313 | False |
| 969974 | 2:640986_CACAT_C | 2 | 640986 | C | CACAT | 0.9006 | -0.0946 | 0.0150 | 2.665000e-10 | 191764 | ---- | 3860399 | 2000640986 | 1 | rs72156956 | False |
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gwascatalog = pd.DataFrame(records,columns=["SNPID","CHR","POS","REPORT_GENENAME","CLOSEST_GENENAMES","FUNCTION_CLASS","OR","BETA","SE","P","TRAIT","STUDY","PUBMEDID"])
sigs = gl.Sumstats(gwascatalog,fmt="gwaslab",other=['REPORT_GENENAME', 'CLOSEST_GENENAMES','TRAIT', 'STUDY', 'PUBMEDID'],verbose=False)
sigs.fix_pos(verbose=False)
sigs.fix_chr(verbose=False)
gwascatalog = pd.DataFrame(records,columns=["SNPID","CHR","POS","REPORT_GENENAME","CLOSEST_GENENAMES","FUNCTION_CLASS","OR","BETA","SE","P","TRAIT","STUDY","PUBMEDID"])
sigs = gl.Sumstats(gwascatalog,fmt="gwaslab",other=['REPORT_GENENAME', 'CLOSEST_GENENAMES','TRAIT', 'STUDY', 'PUBMEDID'],verbose=False)
sigs.fix_pos(verbose=False)
sigs.fix_chr(verbose=False)
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sigs.data
sigs.data
Out[147]:
| SNPID | CHR | POS | BETA | SE | P | OR | STATUS | REPORT_GENENAME | CLOSEST_GENENAMES | TRAIT | STUDY | PUBMEDID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | rs75021220 | 4 | 110737238 | NaN | NaN | 1.000000e-25 | 1.400 | 9990999 | PITX2 | PITX2 | Ischemic stroke (cardioembolic) | Identification of additional risk loci for str... | 27068588 |
| 1 | rs7193343 | 16 | 72995261 | NaN | NaN | 2.000000e-10 | 1.170 | 9990999 | ZFHX3 | HCCAT5 | Ischemic stroke (cardioembolic) | Loci associated with ischaemic stroke and its ... | 26708676 |
| 2 | rs2200733 | 4 | 110789013 | NaN | NaN | 3.000000e-32 | 1.370 | 9990999 | PITX2 | PITX2,MIR297 | Ischemic stroke (cardioembolic) | Loci associated with ischaemic stroke and its ... | 26708676 |
| 3 | rs74475935 | 16 | 15961249 | NaN | NaN | 5.000000e-11 | 4.630 | 9990999 | ABCC1 | LOC107984869 | Ischemic stroke (undetermined subtype) | Loci associated with ischaemic stroke and its ... | 26708676 |
| 4 | rs10744777 | 12 | 111795214 | NaN | NaN | 3.000000e-09 | 1.170 | 9990999 | ALDH2 | MIR6761,ACAD10 | Ischemic stroke (small artery occlusion) | Loci associated with ischaemic stroke and its ... | 26708676 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 193 | rs11880613 | 19 | 10793296 | 0.0613 | 0.011 | 3.000000e-08 | NaN | 9990999 | None | MIR4748,MIR199A1 | Ischemic stroke | A cross-population atlas of genetic associatio... | 34594039 |
| 194 | rs3897976 | 3 | 41565201 | NaN | NaN | 3.000000e-08 | 2.162 | 9990999 | None | ALCAM | Ischemic stroke in dyslipidemia | Identification of susceptibility loci for card... | 33632238 |
| 195 | rs149608518 | 4 | 164551523 | NaN | NaN | 1.000000e-08 | 5.587 | 9990999 | None | RNU6-284P | Ischemic stroke in diabetes mellitus | Identification of susceptibility loci for card... | 33632238 |
| 196 | rs189668056 | 2 | 38081685 | NaN | NaN | 4.000000e-08 | 2.027 | 9990999 | None | CYP1B1,LOC105374466 | Cardio-cerebrovascular disease in dyslipidemia | Identification of susceptibility loci for card... | 33632238 |
| 197 | rs139293840 | 17 | 80556559 | NaN | NaN | 2.000000e-08 | 3.511 | 9990999 | None | RPL32P31,LOC105371922 | Cardio-cerebrovascular disease in diabetes mel... | Identification of susceptibility loci for card... | 33632238 |
198 rows × 13 columns
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locations = pd.json_normalize(api_response["_embedded"]["associations"],"snps",errors='ignore')[["locations","genomicContexts"]]
locations = pd.json_normalize(api_response["_embedded"]["associations"],"snps",errors='ignore')[["locations","genomicContexts"]]
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Out[135]:
| locations | genomicContexts | |
|---|---|---|
| 0 | [{'chromosomeName': '8', 'chromosomePosition':... | [{'isIntergenic': False, 'isUpstream': False, ... |
| 1 | [{'chromosomeName': '19', 'chromosomePosition'... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| 2 | [{'chromosomeName': '8', 'chromosomePosition':... | [{'isIntergenic': True, 'isUpstream': False, '... |
| 3 | [{'chromosomeName': '11', 'chromosomePosition'... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| 4 | [{'chromosomeName': '1', 'chromosomePosition':... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| ... | ... | ... |
| 68 | [{'chromosomeName': '19', 'chromosomePosition'... | [{'isIntergenic': True, 'isUpstream': False, '... |
| 69 | [{'chromosomeName': '3', 'chromosomePosition':... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| 70 | [{'chromosomeName': '19', 'chromosomePosition'... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| 71 | [{'chromosomeName': 'CHR_HSCHR6_MHC_DBB_CTG1',... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| 72 | [{'chromosomeName': 'CHR_HSCHR6_MHC_MCF_CTG1',... | [{'isIntergenic': True, 'isUpstream': False, '... |
73 rows × 2 columns
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layer1 = pd.json_normalize(api_response["_embedded"]["associations"])
layer1
layer1 = pd.json_normalize(api_response["_embedded"]["associations"])
layer1
Out[77]:
| range | snps | loci | riskFrequency | pvalueDescription | orPerCopyNum | snpType | multiSnpHaplotype | snpInteraction | pvalueMantissa | ... | study.publicationInfo.title | study.publicationInfo.author.fullname | study.publicationInfo.author.orcid | _links.self.href | _links.association.href | _links.association.templated | _links.snps.href | _links.backgroundEfoTraits.href | _links.study.href | _links.efoTraits.href | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | [1.65-2.5] | [{'rsId': 'rs12678747', 'merged': 0, 'function... | [{'haplotypeSnpCount': None, 'description': 'S... | NR | None | 2.03 | novel | False | False | 3 | ... | Genome-Wide association between EYA1 and Aspir... | Bourgeois S | None | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | True | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... |
| 1 | NR | [{'rsId': 'rs681343', 'merged': 0, 'functional... | [{'haplotypeSnpCount': None, 'description': 'S... | 0.49 | None | 0.92 | novel | False | False | 2 | ... | GWAS of peptic ulcer disease implicates Helico... | Wu Y | 0000-0002-5977-1526 | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | True | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... |
| 2 | NR | [{'rsId': 'rs2976388', 'merged': 0, 'functiona... | [{'haplotypeSnpCount': None, 'description': 'S... | 0.58 | None | 1.09 | novel | False | False | 2 | ... | GWAS of peptic ulcer disease implicates Helico... | Wu Y | 0000-0002-5977-1526 | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | True | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... |
| 3 | NR | [{'rsId': 'rs10500661', 'merged': 0, 'function... | [{'haplotypeSnpCount': None, 'description': 'S... | 0.8 | None | 0.90 | novel | False | False | 4 | ... | GWAS of peptic ulcer disease implicates Helico... | Wu Y | 0000-0002-5977-1526 | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | True | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... |
| 4 | NR | [{'rsId': 'rs147048677', 'merged': 0, 'functio... | [{'haplotypeSnpCount': None, 'description': 'S... | 0.94 | None | 0.86 | novel | False | False | 9 | ... | GWAS of peptic ulcer disease implicates Helico... | Wu Y | 0000-0002-5977-1526 | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | True | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 68 | None | [{'rsId': 'rs11083749', 'merged': 0, 'function... | [{'haplotypeSnpCount': None, 'description': 'S... | NR | None | NaN | novel | False | False | 3 | ... | A large-scale genome-wide cross-trait analysis... | Adewuyi EO | 0000-0002-4533-0340 | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | True | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... |
| 69 | None | [{'rsId': 'rs709210', 'merged': 0, 'functional... | [{'haplotypeSnpCount': None, 'description': 'S... | NR | None | NaN | novel | False | False | 4 | ... | A large-scale genome-wide cross-trait analysis... | Adewuyi EO | 0000-0002-4533-0340 | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | True | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... |
| 70 | None | [{'rsId': 'rs11880948', 'merged': 0, 'function... | [{'haplotypeSnpCount': None, 'description': 'S... | NR | None | NaN | novel | False | False | 6 | ... | A large-scale genome-wide cross-trait analysis... | Adewuyi EO | 0000-0002-4533-0340 | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | True | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... |
| 71 | None | [{'rsId': 'rs28732222', 'merged': 0, 'function... | [{'haplotypeSnpCount': None, 'description': 'S... | NR | None | NaN | novel | False | False | 1 | ... | A large-scale genome-wide cross-trait analysis... | Adewuyi EO | 0000-0002-4533-0340 | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | True | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... |
| 72 | None | [{'rsId': 'rs17206350', 'merged': 0, 'function... | [{'haplotypeSnpCount': None, 'description': 'S... | NR | None | NaN | novel | False | False | 1 | ... | A large-scale genome-wide cross-trait analysis... | Adewuyi EO | 0000-0002-4533-0340 | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | True | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... | https://www.ebi.ac.uk/gwas/rest/api/associatio... |
73 rows × 46 columns
In [78]:
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layer2 = pd.json_normalize(api_response["_embedded"]["associations"],"snps")
layer2
layer2 = pd.json_normalize(api_response["_embedded"]["associations"],"snps")
layer2
Out[78]:
| rsId | merged | functionalClass | lastUpdateDate | locations | genomicContexts | |
|---|---|---|---|---|---|---|
| 0 | rs12678747 | 0 | intron_variant | 2022-03-23T17:19:52.947+0000 | [{'chromosomeName': '8', 'chromosomePosition':... | [{'isIntergenic': False, 'isUpstream': False, ... |
| 1 | rs681343 | 0 | stop_gained | 2022-08-03T16:59:51.731+0000 | [{'chromosomeName': '19', 'chromosomePosition'... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| 2 | rs2976388 | 0 | intron_variant | 2021-06-24T21:12:27.021+0000 | [{'chromosomeName': '8', 'chromosomePosition':... | [{'isIntergenic': True, 'isUpstream': False, '... |
| 3 | rs10500661 | 0 | intergenic_variant | 2021-06-25T04:18:24.804+0000 | [{'chromosomeName': '11', 'chromosomePosition'... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| 4 | rs147048677 | 0 | synonymous_variant | 2021-06-24T21:12:28.862+0000 | [{'chromosomeName': '1', 'chromosomePosition':... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| ... | ... | ... | ... | ... | ... | ... |
| 68 | rs11083749 | 0 | intron_variant | 2022-09-09T17:04:12.767+0000 | [{'chromosomeName': '19', 'chromosomePosition'... | [{'isIntergenic': True, 'isUpstream': False, '... |
| 69 | rs709210 | 0 | missense_variant | 2022-09-09T17:04:14.640+0000 | [{'chromosomeName': '3', 'chromosomePosition':... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| 70 | rs11880948 | 0 | intron_variant | 2022-09-09T17:04:14.970+0000 | [{'chromosomeName': '19', 'chromosomePosition'... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| 71 | rs28732222 | 0 | downstream_gene_variant | 2022-09-09T17:04:19.712+0000 | [{'chromosomeName': 'CHR_HSCHR6_MHC_DBB_CTG1',... | [{'isIntergenic': True, 'isUpstream': True, 'i... |
| 72 | rs17206350 | 0 | upstream_gene_variant | 2022-09-09T17:04:24.164+0000 | [{'chromosomeName': 'CHR_HSCHR6_MHC_MCF_CTG1',... | [{'isIntergenic': True, 'isUpstream': False, '... |
73 rows × 6 columns
In [80]:
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layer3 = pd.json_normalize(api_response["_embedded"]["associations"],["snps","locations"])
layer3
layer3 = pd.json_normalize(api_response["_embedded"]["associations"],["snps","locations"])
layer3
Out[80]:
| chromosomeName | chromosomePosition | region.name | _links.snps | _links.snps.href | _links.snps.templated | |
|---|---|---|---|---|---|---|
| 0 | 8 | 71202541 | 8q13.3 | [{'href': 'https://www.ebi.ac.uk/gwas/rest/api... | NaN | NaN |
| 1 | 8 | 71202541 | 8q13.3 | [{'href': 'https://www.ebi.ac.uk/gwas/rest/api... | NaN | NaN |
| 2 | 19 | 48703205 | 19q13.33 | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 3 | 8 | 142678838 | 8q24.3 | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 4 | 11 | 6252514 | 11p15.4 | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| ... | ... | ... | ... | ... | ... | ... |
| 107 | 6 | 32707290 | 6p21.32 | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 108 | CHR_HSCHR6_MHC_DBB_CTG1 | 32686300 | None | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 109 | CHR_HSCHR6_MHC_COX_CTG1 | 32630720 | None | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 110 | CHR_HSCHR6_MHC_QBL_CTG1 | 32635972 | None | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 111 | CHR_HSCHR6_MHC_SSTO_CTG1 | 32799385 | None | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
112 rows × 6 columns
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layer3.drop_duplicates(subset=["chromosomeName","chromosomePosition"])
layer3.drop_duplicates(subset=["chromosomeName","chromosomePosition"])
Out[82]:
| chromosomeName | chromosomePosition | region.name | _links.snps | _links.snps.href | _links.snps.templated | |
|---|---|---|---|---|---|---|
| 0 | 8 | 71202541 | 8q13.3 | [{'href': 'https://www.ebi.ac.uk/gwas/rest/api... | NaN | NaN |
| 2 | 19 | 48703205 | 19q13.33 | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 3 | 8 | 142678838 | 8q24.3 | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 4 | 11 | 6252514 | 11p15.4 | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 5 | 1 | 155192003 | 1q22 | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| ... | ... | ... | ... | ... | ... | ... |
| 107 | 6 | 32707290 | 6p21.32 | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 108 | CHR_HSCHR6_MHC_DBB_CTG1 | 32686300 | None | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 109 | CHR_HSCHR6_MHC_COX_CTG1 | 32630720 | None | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 110 | CHR_HSCHR6_MHC_QBL_CTG1 | 32635972 | None | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
| 111 | CHR_HSCHR6_MHC_SSTO_CTG1 | 32799385 | None | NaN | https://www.ebi.ac.uk/gwas/rest/api/singleNucl... | True |
109 rows × 6 columns
In [41]:
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for i in api_response["_embedded"]["associations"]:
if "pvalue" in i:
if float(i["pvalue"])<5e-8:
snp = pd.json_normalize(api_response["_embedded"]["associations"][0]["snps"],max_level=0)
if "rsId" in i["snps"][0]:
print(i["snps"][0]["rsId"], end=",")
if "locations" in i["snps"][0]:
if len(i["snps"][0]["locations"])>0:
print(i["snps"][0]["locations"][0]["chromosomeName"],end=" ")
if len(i["snps"][0]["locations"])>0:
print(i["snps"][0]["locations"][0]["chromosomePosition"],end=" ")
if "genomicContexts" in i["snps"][0]:
if "gene" in i["snps"][0]["genomicContexts"][0]:
if "geneName" in i["snps"][0]["genomicContexts"][0]["gene"]:
print(i["snps"][0]["genomicContexts"][0]["gene"]["geneName"])
print(i["pvalue"])
for i in api_response["_embedded"]["associations"]:
if "pvalue" in i:
if float(i["pvalue"])<5e-8:
snp = pd.json_normalize(api_response["_embedded"]["associations"][0]["snps"],max_level=0)
if "rsId" in i["snps"][0]:
print(i["snps"][0]["rsId"], end=",")
if "locations" in i["snps"][0]:
if len(i["snps"][0]["locations"])>0:
print(i["snps"][0]["locations"][0]["chromosomeName"],end=" ")
if len(i["snps"][0]["locations"])>0:
print(i["snps"][0]["locations"][0]["chromosomePosition"],end=" ")
if "genomicContexts" in i["snps"][0]:
if "gene" in i["snps"][0]["genomicContexts"][0]:
if "geneName" in i["snps"][0]["genomicContexts"][0]["gene"]:
print(i["snps"][0]["genomicContexts"][0]["gene"]["geneName"])
print(i["pvalue"])
rs12678747,8 71202541 EYA1 3e-11 rs681343,19 48703205 FAM83E 2e-15 rs2976388,8 142678838 SLURP2 2e-14 rs10500661,11 6252514 OR52B2 4e-14 rs147048677,1 155192003 HMGN2P18 9e-12 rs78459074,CHR_HSCHR11_2_CTG1 1036727 AP2A2 3e-10 rs34074411,17 41710996 GAST 3e-10 rs687621,9 133261662 MED22 1e-09 rs9581957,13 27983752 CDX2 4e-09 rs200964,6 27899165 H4C11 3.0000000000000003e-13 rs967823,17 52239916 CA10 2e-12 chr19:18793695,
--------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipython-input-41-786e8dc656a6> in <module> 10 print(i["snps"][0]["locations"][0]["chromosomePosition"],end=" ") 11 if "genomicContexts" in i["snps"][0]: ---> 12 if "gene" in i["snps"][0]["genomicContexts"][0]: 13 if "geneName" in i["snps"][0]["genomicContexts"][0]["gene"]: 14 print(i["snps"][0]["genomicContexts"][0]["gene"]["geneName"]) IndexError: list index out of range
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for item,associations in api_response["_embedded"].items():
associations[0]["snp"][0]["rsID"]
for item,associations in api_response["_embedded"].items():
associations[0]["snp"][0]["rsID"]
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In [48]:
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import re
pattern = re.compile(r'rs[0-9]+')
import re
pattern = re.compile(r'rs[0-9]+')
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a=pattern.search("'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs17206350{?projection}'")
a[0]
a=pattern.search("'https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs17206350{?projection}'")
a[0]
rs17206350
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for value in api_response["_embedded"].values():
for i in value:
a=pattern.search(i['loci'][0]["strongestRiskAlleles"][0]["_links"]["snp"]["href"])
if a is None:
continue
base_url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/"+a[0]
r = requests.get(base_url)
snp_response = json.loads(r.text)
print(a[0],snp_response["locations"][0]["chromosomeName"],api_response["locations"][0]["chromosomePosition"])
for value in api_response["_embedded"].values():
for i in value:
a=pattern.search(i['loci'][0]["strongestRiskAlleles"][0]["_links"]["snp"]["href"])
if a is None:
continue
base_url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/"+a[0]
r = requests.get(base_url)
snp_response = json.loads(r.text)
print(a[0],snp_response["locations"][0]["chromosomeName"],api_response["locations"][0]["chromosomePosition"])
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-55-257d03c020b1> in <module> ----> 1 for value in api_response["_embedded"].values(): 2 for i in value: 3 a=pattern.search(i['loci'][0]["strongestRiskAlleles"][0]["_links"]["snp"]["href"]) 4 if a is None: 5 continue KeyError: '_embedded'
In [40]:
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base_url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs3852865"
# Perform POST request and check status code of response
r = requests.get(base_url)
print(r.status_code)
# Transform API response from JSON into Python dictionary and print in console
api_response = json.loads(r.text)
print(api_response["locations"][0]["chromosomeName"],api_response["locations"][0]["chromosomePosition"])
base_url = "https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs3852865"
# Perform POST request and check status code of response
r = requests.get(base_url)
print(r.status_code)
# Transform API response from JSON into Python dictionary and print in console
api_response = json.loads(r.text)
print(api_response["locations"][0]["chromosomeName"],api_response["locations"][0]["chromosomePosition"])
200 19 51210809
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